Resolution enhancement of facial image using an error back-projection of example-based learning
Jeong-Seon Park, Seong–Whan Lee · 2004
This work proposes a new method of enhancing the resolution of facial image from a low-resolution facial image using a recursive error back-projection of example-based learning. A face is represented by a linear combination of prototypes of shape and texture. With the shape and texture information about the pixels in a given low-resolution facial image, we can estimate optimal coefficients for a linear combination of prototypes of shape and those of texture by solving least square minimization. Then high-resolution facial image can be obtained by using the optimal coefficients for linear combination of the high-resolution prototypes, in addition to, a recursive error back-projection is applied to improve the accuracy of resolution enhancement. The encouraging results of the proposed method show that our method can be used to improve the performance of the face recognition by applying our method to enhance the low-resolution facial images captured at visual surveillance systems.